Improving predicted risk of recurrence using molecular profiling in papillary thyroid cancer

Zhijie Li; Guillermo M Ng Yi; Victoria L Deters; Jeremy Chang; Andy Tran; Colin Kenny; Terry Braun; Ronald J Weigel; Anna C Beck

doi:10.1038/s41598-026-50784-9

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Improving predicted risk of recurrence using molecular profiling in papillary thyroid cancer

Journal article

Open access

Peer reviewed

Improving predicted risk of recurrence using molecular profiling in papillary thyroid cancer

Zhijie Li, Guillermo M Ng Yi, Victoria L Deters, Jeremy Chang, Andy Tran, Colin Kenny, Terry Braun, Ronald J Weigel and Anna C Beck

Scientific reports

05/06/2026

DOI: 10.1038/s41598-026-50784-9

PMID: 42091941

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Abstract

Molecular testing can refine the prediction of cancer recurrence. We sought to compare patterns of gene expression in patients with and without recurrence of well-differentiated thyroid cancer to identify pathways associated with recurrence and develop a predictive model based on gene expression. RNA was extracted and sequenced from archival tumor samples of patients well-differentiated thyroid cancer with (n = 8) and without (n = 8) recurrence, all of whom appear clinically at high risk for recurrence. A predictive model was developed using machine learning (ML) with the Thyroid Carcinoma TCGA PanCancer Atlas dataset and externally validated using archival samples. RNA-seq analysis from archival patient samples demonstrated gene expression patterns with striking sex-dependent differences. In tumors from female patients, the TNFα pathway was activated whereas tumors from males showed inhibition of TNFα and estradiol pathways, with findings externally validated through analysis of TCGA data. A prediction model based on TCGA data in female patients was developed that demonstrated an AUC of 0.88 in an external validation cohort for predicting recurrence, providing prognostic information that improves predictions beyond standard clinical parameters. Sex-dependent differences, specifically in TNFα and estrogen response pathways, in thyroid cancer recurrence have important implications for prognosis and treatment.

Machine Learning

Recurrence

TNFα

Well-differentiated thyroid cancer

Papillary thyroid carcinoma

Predictive model

Details

Title: Subtitle: Improving predicted risk of recurrence using molecular profiling in papillary thyroid cancer
Creators: Zhijie Li - University of Iowa
Guillermo M Ng Yi - University of Iowa
Victoria L Deters - University of Iowa
Jeremy Chang - University of Iowa
Andy Tran - University of Iowa
Colin Kenny - University of Iowa
Terry Braun - University of Iowa
Ronald J Weigel - University of Iowa
Anna C Beck - University of Iowa
Resource Type: Journal article
Publication Details: Scientific reports
DOI: 10.1038/s41598-026-50784-9
PMID: 42091941
ISSN: 2045-2322
eISSN: 2045-2322
Publisher: Springer Nature
Grant note: P30CA 86862 / National Cancer Institute of the National Institutes of Health under the Cancer Center Support Grant
Language: English
Electronic publication date: 05/06/2026
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Molecular Physiology and Biophysics; Anatomy and Cell Biology; Surgery; Biochemistry and Molecular Biology
Record Identifier: 9985161447002771

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